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MSDS 434 Data Science and Cloud Computing

Course Description #

This course introduces technologies and systems for developing and implementing data science solutions. It takes a cloud-native approach to delivering analytics applications that are scalable, highly available, and easy to maintain. Students work on systems integration projects, automating stages of application development and using open-source programming languages and systems. They learn about continuous integration and continuous delivery (CI/CD) in the cloud, employing best practices in software engineering. Recommended prior courses: (A) MSDS 431-DL Data Engineering with Go, (B) MSDS 432-DL Foundations of Data Engineering, and (C) MSDS 422-DL Practical Machine Learning or CIS 435 Practical Data Science Using Machine Learning. Prerequisites: (1) MSDS 400-DL Math for Modelers and (2) MSDS 420-DL Database Systems or CIS 417 Database Systems Design.

Students benefit by taking the Go Learning Studio and MSDS 431 Data Engineering with Go before taking this course.

Course Content across Ten Weeks #

  • Week 1. Introduction to Cloud Native Platforms
  • Week 2. Workflow Development
  • Week 3. Platform as a Service and Containerized Development in Cloud Native
  • Week 4. Cloud-Native Database Choice and Design
  • Week 5. Applied Data Engineering
  • Week 6. Managed Machine Learning Platforms
  • Week 7. Microservice-based Machine Learning Model Implementation
  • Week 8. Asynchronous Microservice Application Development
  • Week 9. Application and System Monitoring
  • Week 10. Final Deployment and Maintenance

Students develop a full-stack, end-to-end data science application for delivery through the cloud.

Primary Textbooks #

  • Andrawos, Mina, and Martin Helmich. 2017. Cloud Native Programming with Golang: Develop Microservice-based High Performance Web Apps for the Cloud with Go. Birmingham, UK: Packt Publishing. [ISBN: 978-1787125988]
  • Lakshmanan, Valliappa. 2020. Data Science on the Google Cloud Platform: Implementing End-to-End Real-Time Data Pipelines: From Ingest to Machine Learning (second edition). Sebastopol, CA: O’Reilly. [ISBN-13: 978-1098118952]

Go to the home page Learning Go for Data Science.

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